Cryptocurrency, a decentralized digital currency, has brought about significant social impacts. This essay explores its effects on crime rates, global economic integration, and the GPU market while also delving into the underlying technologies of blockchain and cryptography. The introduction introduces the concept of cryptocurrency as a decentralized, pseudo-anonymous digital currency. The historical journey and the impacts of cryptocurrency are discussed in section 2, focusing on its social implications. The technological underpinnings are further discussed in section 3. The paper concludes by addressing the core technologies: cryptography and blockchain. Cryptography ensures security and anonymity, with SHA-256 as a fundamental algorithm. Blockchain, a decentralized database, interlinks transaction blocks, making tampering difficult due to Proof-of-Work validation. In essence, cryptocurrency’s social impacts and technological foundations intersect, offering insights into its intricate landscape.
In today’s highly interconnected digital environment, computer network information security faces multiple threats such as data breaches, identity forgery, and malicious attacks. As a decentralized and tamper-proof distributed ledger system, blockchain technology provides a new technical path for data security through its core features of cryptographic algorithms and consensus mechanisms. This technology can ensure the integrity of information during data transmission and storage, effectively enhance the overall credibility of network systems, and inject new vitality into the information security protection system. Based on this, this paper explores the implementation of blockchain technology in computer network information security.
Cryptocurrency being a digital or virtual currency that uses cryptography to secure transactions and control the creation of new units. Bitcoin, one of the most popular cryptocurrency, offers various advantages such as security, transparency, and efficiency. The value of Bitcoin can change over time, similar to the regular currencies, and the need to predict the value can be as important as those in the regular. The prediction can be done by multiple algorithms. The purpose of this research is to compare five algorithms in predicting bitcoin value based on Root Mean Squared Error (RMSE) and Squared Error (R2). The five algorithms compared can model the prediction of changes in the bitcoin cryptocurrency, effectively. Based on the experiment, Random Forest outperformed the other algorithms based on its RMSE and R2 result
The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users’ resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm’s efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
Blockchain technology can create a shared platform for English translation and reserve a large number of practical corpus resources, thus improving the quality of machine translation. This paper first introduces the research status of foreign language corpus and blockchain English translation in China. Then it introduces the basic principles of BPNN and particle swarm optimization and constructs the PSO-BP model. Experiments show that the prediction accuracy of BPNN optimized by particle swarm optimization algorithm is greatly improved, the convergence speed is faster, and it will not fall into the local optimal trap. Finally, this paper proposes the implementation path of blockchain in corpus translation application: (1) build “blockchain+ AI” English translation corpus and (2) improve the machine English translation software of the “blockchain+ AI” English translation training platform.
This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
To solve the problem of unreasonable distribution of PoS block rewards, a proof of stake based on incentive (Incentive-PoS)consensus algorithm was proposed. Firstly, the research problem was described. A PoS determined that nodes with more coins have a greater chance of obtaining accounting rights, and the block reward was exclusively owned by the block producer. Secondly, in order to solve the problem of reward distribution, a PoS consensus algorithm based on incentive mechanism was proposed, Shapley′s principle in game theory was uesd to redistribute block rewards. Nodes with high credibility and active participation in consensus would receive dividends, and made small nodes more likely to obtain benefits. Finally, the simulation experiment and result analysis of the improved algorithm were carried out. Compared with the original algorithm, the improved scheme had a more reasonable performance in the distribution of income, and increased the number of nodes receiving dividends, reduced the gap between the rich and the poor, and improved the enthusiasm of consensus. And the throughput, latency, and security were significantly improved. It was beneficial to improve the stratification phenomenon caused by the excessive wealth gap in the blockchain, and could further promote the healthy operation and development of the blockchain network.
The recent revolution in Industry 4.0 (IR 4.0) has characterized the integration of advance technologies to bring the fourth industrial revolution to scale the manufacturing landscape. There are different key drivers for this revolution, in this research we have explored the following among them such as, Industrial Internet of Things (IIoT), Deep Learning, Blockchain and Augmented Reality. The emerging concept from blockchain namely “Non-Fungible Token” (NFT) relating to the uniqueness of digital assets has vast potential to be considered for physical assets identification and authentication in the IR 4.0 scenario. Similarly, the data acquired through the deployment of IIoT devices and sensors into smart industry spectrum can be transformed to generated robust analytics for different industry use-cases. The predictive maintenance is a major scenario in which early equipment failure detection using deep learning model on acquired data from IIoT devices has major potential for it. Similarly, the augmented reality can be able to provide real-time visualization within the factory environment to gather real-time insight and analytics from the physical equipment for different purposes. This research initially conducted a survey to analyse the existing developments in these domains of technologies to further widen its horizon for this research. This research developed and deployed a smart contract into an ethereum blockchain environment to simulate the use-case for NFT for physical assets and processes synchronization. The next phase was deploying deep learning algorithms on a dataset having data generated from IIoT devices and sensors. The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. Lastly, the research also proposed an AR based framework for the visualization ecosystem within the industry environment to effectively visualize and monitory IIoT based equipment’s for different industrial use-cases i.e., monitoring, inspection, quality assurance.
J. D. Preece, Christopher J. Morris, John M. Easton
The System for Ticketing Ubiquity with Blockchains (STUB) is a novel solution to multi-modal transport ticketing. Introduced previously using Hyperledger Fabric, STUB utilises the distributed mechanics of blockchain technology right at the core of its architecture, allowing stakeholders from different transport modes to vend and validate tickets on a shared ledger. This open approach to ticketing data will benefit transport governing bodies, transport operators, and passengers alike by ensuring cross-party cooperation and presenting a fresh holistic approach to the ticketing sector. This paper addresses issues from STUB 1.0, concerning validating tickets for a multi-modal transport system. To overcome this, we propose creating a graph structure, known as the Transport Network Graph (TNG), to represent the transport network with all of the services provided by the Transport Service Providers (TSPs). This enables the implementation of an automated Revenue Allocation System (RAS), whilst retaining the benefits provided by blockchain technology.
Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a "Proof of Learning" consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
The consensus algorithm, as the core technology of blockchain, provides mechanism support and guarantee for the realization of functions such as decentralization, openness, autonomy, information tamperability and anonymous traceability, and realizes efficient achievement of strong and final consistency in distributed system. The consensus algorithms are divided into the previous classical distributed consensus algorithms and the subsequent blockchain consensus algorithms by taking the emergence of bitcoin as the time node. On this basis, the consensus algorithms are further classified according to the implementation principle, the typical algorithms are selected, and then the discussion is focused on decentralization, scalability, security, consistency and so on. Firstly, a general model of blockchain consensus algorithm is proposed, and the basic definition of consensus algorithm is given. Secondly, while introducing the characteristics of the classical distributed consensus algorithms, the distributed consistency algorithms and their improvements such as the two armed forces problem, the Byzantine generals problem, the FLP impossibility theorem, the CAP theorem and Paxos algorithm are studied, and then the execution process and functional characteristics of the algorithm are analyzed. Thirdly, the blockchain consensus algorithms are divided into POW consensus algorithm, POS consensus algorithm, POW+POS hybrid consensus algorithm and POW/POS+BFT/PBFT hybrid consensus algorithm according to different implementation principles and application scenarios. The algorithm flow is given respectively after selecting representative algorithms in each category, and then the specific application scenarios are deeply analyzed. Finally, the research hotspots and development directions of blockchain consensus algorithms in performance and scalability, incentive mechanism, security and privacy, parallel processing and so on are pointed out.
To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.
This research aims to compare the performance between the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) methods in predicting the Bitcoin exchange rate against the US Dollar (BTC-USD). The data used comes from Yahoo Finance for the period 2017-2022. Each model is built with a comparable architecture and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and prediction accuracy metrics. The results show that the LSTM model performed better on the test data with a MAPE of 3.80% and an accuracy of 96.20%, while the GRU model achieved a MAPE of 5.13% and an accuracy of 94.87%. Although the GRU model performed better on the training data, the LSTM model showed better generalization ability on the testing data. This research provides important insights into the selection of the optimal recurrent neural network architecture for Bitcoin exchange rate prediction which is known for its high volatility.
Blockchain products are more and more widely used. How to reasonably evaluate blockchain products has become a hot issue. The main work of this paper is to establish a set of general evaluation indicators for blockchain products, and analyze the needs of future software systems. Firstly, the paper analyzes the common five-tier architecture adopted by the current blockchain system, namely data layer, network layer, consensus layer, smart contract layer and application layer, and expounds the hierarchical characteristics and technical contents of each layer in detail; Then on this basis, a set of general evaluation indicators is proposed for the current common blockchain products, in which the evaluation indicators can be divided into six items: distributed ledger evaluation indicators, public key password evaluation indicators, point-to-point network technology evaluation indicators, consensus mechanism evaluation indicators, intelligent contract mechanism evaluation indicators and upper layer application evaluation indicators. The establishment of indicators can comprehensively evaluate the availability, security and system performance of blockchain products. Finally, based on the evaluation index, the functional and non functional analysis of the blockchain product evaluation system is carried out, which lays a good foundation for the realization of software in the future. The design of evaluation indicators and the functional analysis of the evaluation system will promote the standardization of blockchain products.
In order to solve the problem of illegal member’s tracking attack, which caused by the vehicle units’ privacy disclosure in vehicular ad hoc networks (VANETs), a vehicle identity authentication protocol based on lightweight group signature was proposed by analysis of topology and communication characteristics of VANETs in this paper, which can authenticate the vehicles anonymously in a fast and efficient way. The protocol has five stages. In the initialization phase, the public/private key pairs and system parameters of the group were generated by the VANETs system, then the group public key and system parameters were distributed to the on-board units by the roadside auxiliary facilities. The group private key was kept by the group manager. When a vehicle unit entered VANETs, the unit’s own identity was submitted to the group manager by the blind signature. A group certificate would be distributed to the vehicle unit by the group manager when authentication passed. In the cooperative communication stage, the vehicle member who owned the group certificates signed the state information with the valid certificate and group public key, then sent it to the nearby vehicle units by the car sensors, and achieved cooperative driving with surrounding vehicles. In the message verification stage, only can the legal vehicle members open the received status information by using group public key, but couldn’t know the true identity of the message sender. In this way, the anonymous communication among vehicles was realized. In the stage of signature verification, when a vehicle unit broadcasted a false message for the purpose of exclusively using road resource and caused traffic accident, the group manager can open the signature of the message by using the group private key, and traversed the corresponding vehicle members to carry on the accountability. The innovation of the paper was the usage of improved lightweight group signature technology, which could ensure that the length of group public key and group signature didn’t depend on the number of group members. Zero knowledge proof was also used as a means of membership authentication which improved the speed of authentication among the members. The security of the protocol was analyzed and proved mathematically in this paper, and a LAN simulation platform composed of 100 PC machines was built to simulate the cooperative communication among vehicle units in VANETs. The experimental results showed that authentication time of the protocol was about 7 ms among 100 vehicle users. The performance of the proposed protocol is superior to the contrasted schemes. It greatly reduced the storage and calculation burden of the vehicle units during the process of identity authentication.